Particulate filters are used in a wide range of applications to reduce particulate matter emissions from engines and other combustion sources. Particulate matter or soot emissions consist of solid- or liquid-phase aerosol particles and may be composed of carbon, sulfates, condensed organic components, and inorganic ash. Increasingly stringent particulate matter emission limits require significant reductions in particulate matter emissions. While improved combustion, cleaner fuels, and various in-cylinder strategies provide some reduction in particle emissions, particulate filters have emerged as one of the most effective means for meeting these stringent standards.
Control of particulate filter operation is critical to ensure proper filter and engine performance, minimize filter failures, and reduce the fuel economy penalty resulting from use of the filter. As particulate matter accumulates in the filter, exhaust flow is restricted and backpressure increases. This increased backpressure reduces engine fuel economy, and in severe cases, may cause engine malfunction or damage. In order to maintain acceptable engine operation, the filter is cleaned by oxidizing the particulate matter (regeneration). Filter regeneration may be either active or passive. In an active system, the exhaust temperature is increased by some external means, through the use of a burner, fuel injection over an oxidation catalyst, electric heater, or some other means. In a passive system, the filter may be regenerated through the use of a catalyst either on the filter itself, on a separate substrate, or introduced in the fuel. The catalyst promotes particulate matter oxidation at reduced exhaust temperatures. Accurate monitoring of filter loading is also important in passive systems to ensure the filter is functioning properly.
Accurate knowledge of material accumulation in the particulate filter is important to ensure proper filter regeneration. Excessive soot accumulation in the particulate filter may lead to high internal filter temperatures during regeneration, increasing filter thermal stress, and, in some cases, resulting in filter cracking or melting. On the other hand, regenerating the filter too frequently results in unnecessary fuel economy penalties. Precise measurement of filter loading is, therefore, required to optimize the regeneration strategy.
Aside from soot loading, ash also accumulates in particulate filters. Ash accumulation displaces soot, reducing the filter's soot storage capacity. Ash deposits may alter the distribution of the soot accumulated in the filter. Following extended use, the amount of ash accumulated in the particulate filter may significantly exceed the amount of soot and affect the filter's performance. It may be necessary to periodically remove the ash or replace the filter. Measurement of ash levels in the particulate filter is required to determine filter cleaning or replacement intervals and for accurate control of engine and filter operation.
In addition to monitoring filter loading to control filter and engine operation, it is also important to detect filter failures and malfunctions. Filter failures may occur via the formation of cracks or melting, for example. In some applications, on-board diagnostic requirements stipulate various systems be in place to detect particle leakage from the filter. Further, it may also be necessary to detect the failure or malfunction of individual components, such as sensors for example, required for the proper operation of the engine or after-treatment system. In still other cases, the filter monitoring system, in conjunction with other sensors, may be used to monitor exhaust emissions and provide information useful to modify engine operation based on actual exhaust emission levels, such as for closed loop combustion control.
Currently most Diesel Particulate Filter (DPF) load monitoring systems are based on exhaust pressure drop measurements in conjunction with various predictive models. Pressure drop measurements alone provide only an indirect and imprecise measure of material accumulation in the particulate filter, and suffer from a number of disadvantages. Exhaust gas composition, temperature, and flow rates all affect filter pressure drop and must be accounted for to accurately relate pressure drop to filter soot loading. Further, the distribution of the accumulated ash and soot also affects the pressure drop measurement, and this distribution may change with time, particularly as the filter becomes loaded with ash. Pressure drop measurements are also unable to distinguish between soot and ash accumulation in the filter, the latter of which introduces additional error in soot load estimates based on pressure drop. Additionally, many types of filters exhibit a non-linear pressure drop response and pressure drop hysteresis depending upon the loading state and history of the filter.
Pressure drop-based estimates of soot accumulation in the DPF are also characterized by generally slow response times and low sensitivities to small changes in soot load. Further, the inability of these systems to directly monitor ash levels in the filter requires the filters to be periodically inspected, resulting in vehicle or machine down-time, regardless of the actual filter ash level. Additionally, pressure-drop based measurements are unable to detect all but the most catastrophic of filter failures, and, in most cases, can not meet stringent on-board diagnostic requirements.
In order to address some of the shortcomings inherent to filter pressure drop measurements, various predictive models are generally used in conjunction with these measurements. While various types of models exist, many utilize a number of engine operating parameters, inputs from various engine and exhaust sensors, and the time between regeneration events, to predict the amount of soot accumulated in the DPF. In many cases, these models are uploaded in the engine control unit (ECU). These models are generally calibrated for a specific engine and fuel, requiring recalibration for each specific application. Furthermore, when used with cleaner burning fuels, (compared to the fuel with which the models were initially calibrated) the models tend to over-predict filter soot loading, resulting in unnecessary filter regenerations and fuel economy penalties. The combined use of predictive models and filter pressure drop measurements does little to overcome the deficiencies listed above. These shortcomings lead to inefficient system operation, fuel economy penalties, increased filter thermal cycling and fatigue, and reduced filter service life.
Exhaust gas soot sensors have also been proposed to measure the concentration of soot aerosols directly in the exhaust gas entering the particulate filter. These measurement systems suffer from the deficiency that the amount of soot accumulated on the particulate filter is not necessary equivalent to the amount of soot entering the filter, as some level of passive regeneration may take place, depending on exhaust conditions. Further, exhaust gas soot sensors provide no information on ash accumulation or soot and ash distribution in the DPF. In addition, many of these sensors suffer form soot fouling, consume excessive amounts of energy, and are subject to error introduced by exhaust temperature, exhaust gas velocity, and other factors.
Radio frequency (RF)-based particulate load monitoring systems have also been proposed. One such system monitors filter loading and initiates filter regeneration based on the magnitude of a low-frequency (RF) signal transmitted through the filter. This system, by restricting use to low frequencies below those required to establish resonance in a cavity, overlooks many of the advantages to utilizing higher frequencies required to generate multiple cavity resonant modes.
The use of microwaves to detect soot content in a particulate trap was also proposed. One such system detects soot content in a particulate filter by monitoring a change in filter resonant frequency. However, such systems cannot determine the spatial distribution of soot content within the particulate filter.
All RF and microwave filter load monitoring systems heretofore known suffer from a number of disadvantages:
(a) Prior art systems are unable to monitor the spatial distribution of material accumulated in the filter. Ash accumulation in the filter displaces soot and alters its distribution. Further, non-uniform flow conditions may also result in non-uniform material accumulation.
(b) All known filter load monitoring systems initiate filter regeneration based on some average total filter soot load. Locally high soot loads cannot be detected by systems that are not capable of measuring material distribution in the filter.
(c) Previous microwave- and RF-based filter loading systems cannot simultaneously detect both soot and ash accumulation in the filter over all exhaust conditions.
(d) These systems do not detect filter failures or malfunctions, which is important to ensure the filter is operating as required.
(e) These systems do not detect malfunctions or failures of individual components, such as sensors, for example, that may be required for correct operation of the filter.
(f) Microwave and RF-based measurement systems are strongly affected by moisture content and water vapor present in the exhaust gas and on the filter, which must be accounted for to reduce error in the measurement.
(g) Previous filter load monitoring systems fail to communicate with existing engine and exhaust sensors to provide feedback control capabilities useful to modify engine operation to optimize the combined engine and after-treatment system performance.
Therefore, it would be beneficial if there were a particulate filter load monitoring system that addressed the problems described above. Such a system would be advantageous in that lower emission limits may be achieved, while minimizing the amount of maintenance and unnecessary regeneration cycles. Such a system would be applicable not just for diesel engines but any engine or emission control application where a reduction in soot or particulate matter emissions is required. Further, the present disclosure relates not only a particulate filter load monitoring and control systems, but any type of filter, such as air filters, liquid filters, filter bag houses, and the like, where knowledge of filter loading, by contaminant material or some other matter, and control of filter operation are important.